A day in daylight

Author

Johannes Zauner

Preface

This document contains the analysis and results for the event A day in daylight, where people from around the world measured a complete day of light exposure on (and around) 22 September 2025.

Note

Note that this script is optimized to generate plot outputs and objects to implement in a dashboard. Thus the direct outputs of the script might look distorted in places.

Importing data

We first set up all packages needed for the analysis

library(LightLogR)
library(Hmisc)

Attaching package: 'Hmisc'
The following objects are masked from 'package:base':

    format.pval, units
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ dplyr::src()       masks Hmisc::src()
✖ dplyr::summarize() masks Hmisc::summarize()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(gt)

Attaching package: 'gt'

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library(patchwork)
library(gtExtras)
library(gtsummary)
library(legendry)
library(rlang)

Attaching package: 'rlang'

The following objects are masked from 'package:purrr':

    %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
    flatten_raw, invoke, splice
library(gganimate)
library(ggdist)

Attaching package: 'ggdist'

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library(sf)
Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
library(rnaturalearth)
library(rnaturalearthdata)

Attaching package: 'rnaturalearthdata'

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    countries110
library(ggplot2)
library(glue)
library(suncalc)
library(scales)

Attaching package: 'scales'

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    discard

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library(progress)

# source("https://raw.githubusercontent.com/MeLiDosProject/Data_Metadata_Conventions/main/scripts/overview_plot.R")

Next we import the survey data. Data were collected with REDCap, and there is an import script to load the data in.

source("scripts/prep_survey_data.r")

Connecting light data with survey data

First, we collect a list of available data sets. As we need to compare them to the device ids in the survey, we require only the file without path or extension

path_light <- "data/lightloggers"
files_light <- list.files(path_light) |> tools::file_path_sans_ext()

Next we check which devices are declared in the survey.

survey_devices <- data |> drop_na(device_id) |> pull(device_id) #get devices
survey_devices |> anyDuplicated() #are any entries duplicated?: No
[1] 0
survey_devices |> setequal(files_light) #are light files and survey entries equal?: Yes
[1] TRUE

No entries are duplicated and the survey device Ids are equal to the light files.

Device and location information

Next, we need to get the time zones of the participants and their coordinates. For this, let’s reduce the complexity of the dataset and clean the data

data_devices <- 
data |> 
  drop_na(device_id) |> 
  select(device_id, record_id, 
         city_country, latitude, longitude,
         age, sex = sex.factor,
         complete_log = complete_log.factor,
         behaviour_change = behaviour_change.factor,
         travel_time_zone) |> 
  mutate(travel_time_zone = travel_time_zone == 1)
label(data_devices$travel_time_zone) = "Time zone travel"
label(data_devices$age) = "age"
label(data_devices$behaviour_change) = "Behaviour change"

data_devices |> gt() |> opt_interactive()

Record ID 31 did not finish the post-survey, so we lack data on that device and consequently remove it. Furthermore, Record ID 30 only has data much outside the time frame of interest. Record ID 40 don’t has sensible data and will also be removed. Likely some issue with a dead battery is the reason.

data_devices <- data_devices |> filter(!record_id %in% c("31", "30", "44"))

We also have to clean up the city and country, as well as latitude and longitude data. We do this separately and load the data back in. The manual entries for locations had to be cleaned. This was done with OpenAI through an API key. The results were stored in the file data/cleaned/places.csv. Uncomment the code cell below to recreate the process. Details in outcome may vary, however.

# library(ellmer)
# 
# data_devices_red <- 
# data_devices |> 
#   select(record_id, city_country, latitude, longitude)
# 
# chat <- chat_openai("If there is more then one place specified, only use the first one. If latitude and longitude are misspecified, make a best guess based on city_country. Use IANA names for the time zone identifieres")
# 
# #reducing each line in a table to a single string
# data_devices_red <- 
# data_devices_red |> 
#   pmap(~ paste(paste(names(data_devices_red), c(...), sep = ": "), collapse = ", "))
# 
# #creating an output structure
# type_place <- type_object(
#   record_id = type_string(),
#   city = type_string(),
#   country = type_string(),
#   latitude = type_number(),
#   longitude = type_number(),
#   tz_identifier = type_string(),
#   UTC_dev = type_number("deviation from UTC in hours, given the 22 September 2025")
# )
# 
# places <-
# parallel_chat_structured(
#   chat,
#   data_devices_red,
#   type = type_place
# )
# 
# write.csv(places, "data/cleaned/places.csv")
#read pre-cleaned data in
places <- read_csv("data/cleaned/places.csv")
New names:
Rows: 49 Columns: 8
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(3): city, country, tz_identifier dbl (5): ...1, record_id, latitude,
longitude, UTC_dev
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`
places <- 
  places |> 
  dplyr::mutate(record_id = as.character(record_id)) |> 
  select(-`...1`)

#merge data with main data
data_devices_cleaned <- 
data_devices |> 
  select(-city_country, -latitude, -longitude) |> 
  mutate(record_id = as.character(record_id)) |> 
  left_join(places, by = "record_id") |> 
  mutate(city = case_match(city,
                          "Tuebingen" ~ "Tübingen",
                          "İzmir" ~ "Izmir",
                          .default = city),
         country = case_match(country,
                              "The Netherlands" ~ "Netherlands",
                              c("Turkiye", "Türkiye") ~ "Turkey",
                              c("US", "United States", "USA") ~ 
                                "United States of America",
                              "UK" ~ "United Kingdom",
                              .default = country)
         )

First overview

The following code cells use the data imported so far to create some descriptive plots about the sample.

sex_lab <- primitive_bracket(
  key  = key_range_manual(          # <− positions + labels
    start = c(-7,0.1),
    end = c(-0.1,7),# -6  and  +6  on the x-axis
    name     = c("Males", "Females")
  ),
  position = "bottom"         # draw it at the bottom of the panel
)
Plot_demo <- 
data_devices_cleaned |>  
  mutate(
         age_group = 
           cut(age, 
               breaks = seq(15,70,5), 
               labels = c("18-20", "21-25", "26-30", "31-35", 
                          "36-40", "41-45", "46-50", "51-55", 
                          "56-60", "61-65", "66-70"),
               right = TRUE, ordered_result = TRUE),
         ) |> 
  group_by(sex, age_group) |> 
  dplyr::summarize(n = n(), .groups = "drop") |> 
  mutate(n = ifelse(sex == "Male", -n, n)) |> 
  ggplot(aes(x= age_group, y = n, fill = sex)) + 
  geom_col() +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-6,6, by = 2), 
                     labels = c(6, 4, 2, 0, 2, 4, 6)) +
  scale_fill_manual(values = c(Male = "#2D6D66", Female = "#A23B54")) + 
  guides(fill = "none", alpha = "none",
         x = guide_axis_stack(
           "axis", sex_lab
         )) +
  theme_minimal() +
  coord_flip(ylim = c(-7, 7)) +
  labs(x = "Age (yrs)", y = "n")

Plot_demo

ggsave("figures/Fig1_age.png", 
       width = 6, height = 6, units = "cm", scale = 1.6)
location_stats <- 
data_devices_cleaned |> 
  dplyr::summarise(
    tz = n_distinct(UTC_dev),
    country = n_distinct(country),
    n = n()
  ) |> 
  pivot_longer(
    cols = everything()
  ) |> 
  dplyr::mutate(name = case_match(name,
                           "country" ~ "Countries",
                           "tz" ~ "Time zones",
                           "n" ~ "Participants"),
         name = factor(name, levels = c("Time zones", "Countries", "Participants"))
         )

P_stats <-
location_stats |> 
  ggplot(aes(y = fct_rev(name), x = value, fill = name)) +
  geom_col() +
  geom_text(aes(label = value), color = "white", hjust = 1.2, fontface = 2, size = 3) +
  theme_minimal() +
  theme_sub_panel(grid = element_blank()) +
  theme_sub_axis_bottom(text = element_blank()) + 
  theme_sub_plot(background = element_rect(fill = alpha("white", 0.75))) +
  labs(x = NULL, y = NULL) +
  scale_fill_manual(values = c(`Time zones` = "deepskyblue3",
                               Participants = "red",
                               Countries = "grey")) +
  guides(fill = "none")

P_tz <-
  data_devices_cleaned |> 
    group_by(UTC_dev) |> 
    dplyr::summarise(n = n()) |> 
    ggplot(aes(x=UTC_dev, y = n)) +
    geom_vline(xintercept = 0, col = "grey") +
    geom_hline(yintercept = 0, col = "grey") +
    geom_col(fill = "deepskyblue3")+
    geom_text(aes(label = n), fontface = 2, vjust = -0.2) +
    theme_minimal() +
    theme_sub_panel(grid.major.y = element_blank(),
                    grid.minor = element_blank()) +
    theme_sub_axis_left(text = element_blank()) +
    scale_x_continuous(breaks = seq(-12, 12, 2)) +
    labs(x = "Deviation from UTC (h) on 22 Sep 2025", y = "n") +
    coord_cartesian(xlim = c(-11,11), ylim = c(NA, 30))
world <- ne_countries(scale = "medium", returnclass = "sf")
    
countries_colors <- tibble(
      country = data_devices_cleaned |> dplyr::count(country) |> pull(country),
      color   = "#0073C2FF",
      stringsAsFactors = FALSE
    )
    
world$color <- ifelse(
      world$name %in% countries_colors$country,
      countries_colors$country[match(world$name, countries_colors$country)],
      NA
    )
    
location_info <- tibble(
      country  = data_devices_cleaned |> pull(country),
      lat      = data_devices_cleaned |> pull(latitude),
      lon      = data_devices_cleaned |> pull(longitude),
      color    = "#0073C2FF",
      stringsAsFactors = FALSE
    ) |> 
  mutate(lat2 = plyr::round_any(lat, 12), 
         lon2 = plyr::round_any(lon, 12)) |> 
  dplyr::summarize(
    .by = c(lat2, lon2),
    lat = mean(lat),
    lon = mean(lon),
    color = unique(color),
    n = n()
  )
    
locations <- st_as_sf(location_info, coords = c("lon", "lat"), crs = 4326)
world_proj     <- st_transform(world,    crs = "+proj=eqc")
locations_proj <- st_transform(locations, crs = "+proj=eqc")
bb <- st_bbox(world_proj)
tz <- sf::st_read("data/tz_now/combined-shapefile-with-oceans-now.shp")  # or .gpkg / .geojson
Reading layer `combined-shapefile-with-oceans-now' from data source 
  `/Users/zauner/Documents/Arbeit/12-TUM/2025_ADayInDaylight_Data/data/tz_now/combined-shapefile-with-oceans-now.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 92 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 90
Geodetic CRS:  WGS 84
tz_lines <- sf::st_boundary(tz)

P_map <-
  ggplot() +
  geom_sf(
    data = world_proj,
    # aes(fill = color),
    fill = "grey",
    color = NA,
    size = 0.25,
    alpha = 0.5,
    show.legend = FALSE
  ) +
  geom_sf(data = tz_lines,
          colour = "deepskyblue3",
          linewidth = 0.15) +
  geom_sf(
    data = locations_proj,
    aes(size = n),
    fill = "red",
    alpha = 0.9,
    shape = 21,
    color = "#0073C2FF",
    stroke = 0.2
  ) +
  geom_sf_text(
    data = locations_proj,
    aes(label = n),
    size = 1.5,
    fontface = 2,
    color = "white",
    alpha = 0.75
  ) +
  scale_fill_manual(values = rep("#0073C2FF", 15)) +
  scale_size_continuous(range = c(2, 5)) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(x = NULL, y = NULL) +
  coord_sf(expand = FALSE)
P_map_patch <- 
(P_map + inset_element(P_stats, 0.05, 0.05, 0.25, 0.25)) / P_tz + plot_layout(heights = c(4.4,1))

ggsave("figures/Fig2_location.pdf",P_map_patch,
       width = 15, height = 10, units = "cm", scale = 1.6)
ggsave("figures/Fig2_location.png",
       P_map_patch,
       width = 14, height = 9.5, units = "cm", scale = 1.6)

Import wearable data

Next, we import the light data. There are two devices in use: ActLumus and ActTrust we need to import them separately, as they are using different import functions. device_id with four number indicate the ActLumus devices, whereas with seven numbers the ActTrust. We add a column to the data indicating the Type of device in use. We also make sure that the spelling equals the supported_devices() list from LightLogR. Then we construct filename paths for all files.

c("ActLumus", "ActTrust") %in% supported_devices()
[1] TRUE TRUE
data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(device_type = 
           case_when(
              str_length(device_id) == 4 ~ "ActLumus",
              str_length(device_id) == 7 ~ "ActTrust"
           ),
         file_path = glue("data/lightloggers/{device_id}.txt")
         )
data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::pmap(list(x = device_type, y = file_path, z = tz_identifier), 
                       \(x, y, z) {
                         import_Dataset(device = x, filename = y, tz = z,
                                        silent = TRUE)
                       }
    )
  )

We end with one dataset per row entry. As the two ActTrust files do not contain a melanopic EDI column, we will use the photopic illuminance column LIGHT towards that end. As there are only two participants with this shortcoming, it will not influence results overduly.

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map2(device_type, data, 
                       \(x,y) {
                         if(x == "ActTrust") {
                           y |> dplyr::rename(MEDI = LIGHT)
                         }
                         else y
                         }
                       )
  )

Further, the dataset in Malaysia had a device malfunction on 22 September and only worked from the 23 September onwards. As there are minimal differences between dates and very few datasets in that region, we will not dismiss that dataset but rather shift data by one day. We need to shift the data by another 8 hours backwards, as that dataset was stored in UTC time (for some reason).

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map2(record_id, data, 
                       \(x,y) {
                         if(x == "25") {
                           y |> dplyr::mutate(
                             Datetime = Datetime - ddays(1) - dhours(8)
                             )
                         }
                         else y
                         }
                       )
  )

Light data

Cleaning light data

In this section we will prepare the light data through the following steps:

  • resampling data to 5 minute intervals
  • filling in missing data with explicit gaps
  • removing data that does not fall between 2025-09-21 10:00:00 UTC and 2025-09-23 12:00:00 UTC, which contains all times where 22 September occurs somewhere on the planet
data_devices <-
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map(data, 
                       \(x) {
                           x |> 
                           aggregate_Datetime("5 mins") |> #resample to 5 mins
                           gap_handler(full.days = TRUE) |> #put in explicit gaps
                           filter_Datetime(start = "2025-09-21 10:00:00",
                                           end = "2025-09-23 12:00:00",
                                           tz = "UTC") #cut out a section of data
                         }
                       )
  )

Next, we add a secondary Datetime column that runs on UTC time.

data_devices <-
data_devices |> 
  dplyr::mutate(
    data = purrr::map(data, 
                       \(x) {
                           x |> 
                           dplyr::mutate(Datetime_UTC = Datetime |> force_tz("UTC"))
                         }
                       )
  )

Visualizing light data

Now we can visualize the whole dataset - first by combining all datasets.

start_dt <- as.POSIXct("2025-09-21 10:00:00", tz = "UTC")
start_dt2 <- as.POSIXct("2025-09-22 00:00:00", tz = "UTC")
end_dt <- as.POSIXct("2025-09-23 12:00:00", tz = "UTC")
end_dt2 <- as.POSIXct("2025-09-23 00:00:00", tz = "UTC")
light_data <- 
  join_datasets(!!!data_devices$data) |> 
  mutate(Datetime = Datetime |> with_tz("UTC"))
light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(facetting = FALSE, 
          group = Id, 
          geom = "ribbon",
          lwd = 0.25,
          fill = "skyblue3",
          color = "skyblue4",
          alpha = 0.1,
          y.axis.label = "UTC Time"
          ) +
  geom_vline(xintercept = c(start_dt, end_dt), color = "red")

light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(Datetime_UTC,
          geom = "ribbon",
          facetting = FALSE,
          fill = "skyblue3",
          color = "skyblue4",
          alpha = 0.1,
          group = Id, 
          lwd = 0.25,
          y.axis.label = "Local Time"
          ) +
  geom_vline(xintercept = c(start_dt2, end_dt2), color = "red")

light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(Datetime_UTC,
          facetting = FALSE, 
          group = Id, 
          lwd = 0.25,
          y.axis.label = "UTC Time"
          ) +
  geom_vline(xintercept = c(start_dt2, end_dt2), color = "red")

boundaries <- tibble(
  start = c(start_dt, start_dt2),
  end = c(end_dt, end_dt2),
  name = c("UTC Time", "Local Time")
)

p <- 
light_data |> 
  aggregate_Datetime("2 hours") |>
  select(Id, Datetime, Datetime_UTC, MEDI) |> 
  pivot_longer(-c(Id, MEDI)) |> 
  mutate(name = case_match(name,
                           "Datetime" ~ "UTC Time",
                           "Datetime_UTC" ~ "Local Time")) |> 
  dplyr::mutate(name = factor(name)) |> 
    gg_days(value,
            geom = "ribbon",
            fill = "skyblue3",
            alpha = 0.4,
            color = "black",
          facetting = FALSE, 
          group = Id, 
          lwd = 0.1,
          x.axis.label = "{next_state} {if(transitioning) '(transitioning)' else ''}",
          y.axis.label = "Melanopic EDI (lx)",
          x.axis.breaks = \(x) Datetime_breaks(x, by = "6 hours", shift = 0),
          x.axis.format = "%H:%M"
    )  + 
  geom_vline(data = boundaries, aes(xintercept=start), col = "red", lty = 2, inherit.aes = FALSE)+
  geom_vline(data = boundaries, aes(xintercept=end), col = "red", lty = 2, inherit.aes = FALSE)+
  geom_segment(data = boundaries, 
               aes(y = 25000, x = start, xend = end), 
               arrow = arrow(length = unit(0.1, "inches"), ends = "both"), col = "red",  
               inherit.aes = FALSE)+
  annotate(geom = "text", y = 25000, x = mean(c(start_dt2, end_dt2)), 
           vjust = -0.4, label = "Global 22 September", col = "red") +
  transition_states(
    name, 
    transition_length = 1,
    state_length = 1
  )

if(interactive()){
animation <- 
animate(p, width = 1200, height = 700, res = 150)

animation

anim_save("figures/patterns.gif", animation)
}

Events

Cleaning events

In this section we deal with with the activity logs - first by filtering them out of the dataset, and selecting the relevant aspects.

events <- 
data |> 
  filter(redcap_repeat_instrument == "log_a_new_activity") |> 
  select(record_id,
         type.factor, 
         social_context.factor,
         wear_activity.factor,
         nonwear_activity.factor,
         nighttime.factor,
         setting_level01.factor, 
         setting_level02_indoors.factor,
         setting_level02_indoors_home.factor,
         setting_level02_indoors_workingspace.factor,
         setting_level02_indoors_healthfacility.factor,
         setting_level02_indoors_learningfacility.factor,
         setting_level02_indoors_leisurespace.factor,
         setting_level02_indoors_retailfacility.factor,
         setting_level02_mixed.factor,
         setting_level02_outdoors.factor,
         lighting_scenario_daylight___1.factor,
         lighting_scenario_daylight___2.factor,
         lighting_scenario_daylight___3.factor,
         lighting_scenario_daylight___4.factor,
         lighting_scenario_3___1.factor,
         lighting_scenario_3___2.factor,
         lighting_scenario_3___3.factor,
         lighting_scenario_2___1.factor,
         lighting_scenario_2___2.factor,
         lighting_scenario_2___3.factor,
         lighting_scenario_2___4.factor,
         autonomy.factor,
         notes, 
         startdate, enddate
         )

#adding labels to the factors
label(events$type.factor) = "Wear type: Are you wearing the light logger at the moment?"
label(events$social_context.factor) = "Are you alone or with others?"
label(events$wear_activity.factor) = "Wear activity "
label(events$nonwear_activity.factor) = "Non-wear activity"
label(events$nighttime.factor) = "Where was the light logger when you were asleep?"
label(events$setting_level01.factor) = "Select the setting"
label(events$setting_level02_indoors.factor) = "Indoors setting"
label(events$setting_level02_indoors_home.factor) = "Indoors setting (home)"
label(events$setting_level02_indoors_workingspace.factor) = "Indoors setting (working space)"
label(events$setting_level02_indoors_learningfacility.factor) = "Indoors setting (learning facility)"
label(events$setting_level02_indoors_retailfacility.factor) = "Indoors setting (retail facility)"
label(events$setting_level02_indoors_healthfacility.factor) = "Indoors setting (health facility)"
label(events$setting_level02_indoors_leisurespace.factor) = "Indoors setting (leisure space)"
label(events$setting_level02_outdoors.factor) = "Outdoors setting"
label(events$setting_level02_mixed.factor) = "Indoors-outdoors setting"
label(events$lighting_scenario_daylight___1.factor) = "Select lighting setting (daylight) (choice=Outdoors (direct sunlight))"
label(events$lighting_scenario_daylight___2.factor) = "Select lighting setting (daylight) (choice=Outdoors (in shade / cloudy))"
label(events$lighting_scenario_daylight___3.factor) = "Select lighting setting (daylight) (choice=Indoors (near window / exposed to daylight))"
label(events$lighting_scenario_daylight___4.factor) = "Select lighting setting (daylight) (choice=Indoors (away from window))"
label(events$lighting_scenario_3___1.factor) = "Select lighting setting (electric light) (choice=Lights are switched on)"
label(events$lighting_scenario_3___2.factor) = "Select lighting setting (electric light) (choice=Low-light or dimmed lights)"
label(events$lighting_scenario_3___3.factor) = "Select lighting setting (electric light) (choice=Completed darkness)"
label(events$lighting_scenario_2___1.factor) = "Select lighting setting (screen use) (choice=Smartphone)"
label(events$lighting_scenario_2___2.factor) = "Select lighting setting (screen use) (choice=Tablet)"
label(events$lighting_scenario_2___3.factor) = "Select lighting setting (screen use) (choice=Computer)"
label(events$lighting_scenario_2___4.factor) = "Select lighting setting (screen use) (choice=Television)"
label(events$autonomy.factor) = "Were the lighting conditions in this setting self-selected (i.e., you had control over lighting intensity, spectrum, or exposure)?"

Next, we condense columns that can be expressed as one. We also lose the .factor extension, as now all doubles are removed. Finally, we simplify entries.

events <- 
events |> 
  rename_with(\(x) x |> str_remove(".factor")) |> #remove .factor extension
  dplyr::mutate(
    type = type |> fct_relabel(\(x) str_remove(x, "-time| time| \\(not wearing light logger\\)")),
    across(c(wear_activity, setting_level01),
           \(x) x |> fct_relabel(\(y) str_remove(y, " \\(.*\\)"))
           ),
    nonwear_activity = 
      nonwear_activity |> fct_recode(
        "Dark mobile" = "Left in a bag, or other mobile dark place",
        "Dark stationary" = "Left in a drawer or cabinet, or other stationary dark place",
        "Stationary" = "Left on a table or other surface with varying light exposure"
      ),
    nighttime = 
      nighttime |> fct_recode(
        "Upward" = "Facing upward on bedside table",
        "Downward" = "Facing downward on bedside table"
      ),
    across(c(setting_level02_indoors, setting_level02_outdoors),
           \(x) x |> fct_recode(
        "Leisure" = "Leisure space (sports, recreation, entertainment)",
        "Commercial" = "Retail, food or services facility",
        "Workplace" = "Working space",
        "Education" = "Learning facility",
        "Healthcare" = "Health facility"
           )
        ),
    setting_level01 = 
      setting_level01 |> fct_recode(
        "Mixed" = "Indoor-outdoor setting"
      ),
    autonomy =
      autonomy |> fct_recode(
        Yes = "Yes, fully self-selected (e.g., adjusting lights at home or in a private office, moving to shaded area)",
        Partly = "Partly self-selected (e.g., some control such as opening blinds or switching a desk lamp, but not over main lighting)",
        No = "Not self-selected (e.g., public transport, shared office, classroom, hospital, airplane, etc.)",
        NULL = "Not applicable"
      )
  ) |> 
  dplyr::rename(setting_light = setting_level01)
events <- 
events |>
  dplyr::mutate(
    scenario_daylight =
      case_when(
        (lighting_scenario_daylight___1 == "Checked") & 
          (setting_light %in% c("Outdoors", "Mixed")) ~ "Direct sunlight",
        (lighting_scenario_daylight___2 == "Checked") &
          (setting_light %in% c("Outdoors", "Mixed")) ~ "Shade / cloudy",
        (lighting_scenario_daylight___3 == "Checked") &
          (setting_light %in% c("Indoors", "Mixed")) ~ "Near a window",
        (lighting_scenario_daylight___4 == "Checked") &
          (setting_light %in% c("Indoors", "Mixed")) ~ "Away from window"
      ),
    scenario_electric =
      case_when(
        lighting_scenario_3___3 == "Checked" ~ "Lights off",
        lighting_scenario_3___2 == "Checked" ~ "Dim light",
        lighting_scenario_3___1 == "Checked" ~ "Lights on",
      ),
    across(starts_with("lighting_scenario_2___"),
           \(x) ifelse(x == "Checked", TRUE, FALSE)),
  ) |> 
  dplyr::rename(screen_phone = lighting_scenario_2___1,
                screen_tablet = lighting_scenario_2___2,
                screen_pc = lighting_scenario_2___3,
                screen_tv = lighting_scenario_2___4
                ) |> 
  select(-starts_with("lighting_scenario")) |> 
  dplyr::mutate(
    wear_activity = case_when(type == "Wear" ~ wear_activity, .default = NA),
    nonwear_activity = case_when(type == "Non-wear" ~ nonwear_activity, .default = NA),
    nighttime = case_when(type == "Bedtime" ~ nighttime, .default = NA),
    setting_level02_mixed = case_when(setting_light == "Mixed" ~ setting_level02_mixed, .default = NA),
    setting_level02_indoors = case_when(setting_light == "Indoors" ~ setting_level02_indoors, .default = NA),
    setting_level02_outdoors = case_when(setting_light == "Outdoors" ~ setting_level02_outdoors, .default = NA),
    setting_level02_indoors_leisurespace = case_when(setting_level02_indoors == "Leisure" ~ setting_level02_indoors_leisurespace, .default = NA),
    setting_level02_indoors_workingspace = case_when(setting_level02_indoors == "Workplace" ~ setting_level02_indoors_workingspace, .default = NA),
  ) |> 
  unite("type.detail", c(wear_activity, nonwear_activity, nighttime), na.rm = TRUE,
        remove = FALSE) |> 
  unite("setting_location", 
        c(setting_level02_indoors, setting_level02_outdoors, setting_level02_mixed), 
        na.rm = TRUE, remove = FALSE) |> 
  unite("setting_specific", 
        starts_with("setting_level02_indoors_"), 
        na.rm = TRUE, remove = FALSE) |> 
  dplyr::rename_with(\(x) x |> str_remove("_level02")) |> 
  relocate(scenario_daylight, scenario_electric, .before = screen_phone) |> 
  relocate(startdate, .before = 1) |> 
  select(-enddate) |> 
  dplyr::mutate(
    across(c(setting_location, setting_specific, type.detail),
           \(x) fct_recode(x, NULL = ""))
  )
part_data <- data_devices |> select(-data) |> mutate(record_id = as.character(record_id))

events_complete <- 
events |> 
  dplyr::mutate(record_id = as.character(record_id)) |> 
  left_join(part_data, by = "record_id") |> 
  drop_na(tz_identifier)

label(events_complete$record_id) = "Record ID"

events_complete <- 
events_complete |> 
  dplyr::mutate(
    Datetime = as.POSIXct(startdate, tz = "UTC"),
    UTC_dt = force_tzs(Datetime, tz_identifier),
    .before = 1) |> 
  select(-startdate)

Summaries

In this section we will calculate some summary statistics regarding events

events_complete <- 
events_complete |> 
  dplyr::mutate(status.duration = c(diff(Datetime), na_dbl), 
                .by = record_id,
                .after = Datetime) |> 
  filter(!record_id %in% c("31", "30", "44"))
label(events_complete$status.duration) = "Time between log entries"
label(events_complete$type.detail) = "Wear/Non-wear context"
label(events_complete$setting_location) = "General setting"
label(events_complete$setting_specific) = "Specific indoor setting"
label(events_complete$scenario_daylight) = "Daylight conditions"
label(events_complete$scenario_electric) = "Electric lighting conditions"
label(events_complete$screen_phone) = "Phone use"
label(events_complete$screen_tablet) = "Tablet use"
label(events_complete$screen_pc) = "Computer use"
label(events_complete$screen_tv) = "Television use"
label(events_complete$sex) = "Sex"
label(events_complete$city) = "City"
label(events_complete$country) = "Country"
label(events_complete$latitude) = "Latitude"
label(events_complete$longitude) = "Longitude"
label(events_complete$tz_identifier) = "Time zone identifier"
label(events_complete$UTC_dev) = "Time zone deviation from UTC"
label(events_complete$device_type) = "Used device"
label(events_complete$file_path) = "File path"
label(events_complete$setting_indoors) = "Indoor settings"
label(events_complete$setting_outdoors) = "Outdoor settings"
label(events_complete$setting_mixed) = "Outdoor-Indoor mixed settings"
label(events_complete$wear_activity) = "Activity"
label(events_complete$nonwear_activity) = "Non-wear wearable position"
label(events_complete$nighttime) = "Nightstand wearable measurement direction"
event_summary1 <- 
events_complete |> 
  dplyr::summarize(.by = record_id,
            n = n(),
            mean.duration = mean(status.duration, na.rm = TRUE),
            covered.timespan = last(Datetime) - first(Datetime)
  )
label(event_summary1$n) = "Log entries"
label(event_summary1$mean.duration) = "Mean duration between log entries"
units(event_summary1$mean.duration) = "hours"
label(event_summary1$covered.timespan) = "Total time span of log entries"

event_summary1.tbl <- 
event_summary1 |> 
  tbl_summary(include = -record_id,
              statistic = list(all_continuous() ~ "{mean} ({min}-{max})")
              ) |> 
  modify_caption("**Activity logging (by-participant level)**")

event_summary1.tbl
Activity logging (by-participant level)
Characteristic N = 471
Log entries 38 (9-80)
Mean duration between log entries 1.69 hours (0.53 hours-3.92 hours)
Total time span of log entries 2.09 days (1.14 days-4.13 days)
1 Mean (Min-Max) 
gtsave(event_summary1.tbl |> as_gt(), "tables/table1.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec4995e253512.html screenshot completed
event_tbl_setting <- 
events_complete |> 
  select(setting_indoors, setting_outdoors, setting_mixed
         ) |> 
  tbl_summary(missing = "no"
  )

event_tbl_setting
Characteristic N = 1,7631
Indoor settings
    Home 661 (70%)
    Workplace 126 (13%)
    Education 17 (1.8%)
    Commercial 84 (8.9%)
    Healthcare 3 (0.3%)
    Leisure 23 (2.4%)
    Other 27 (2.9%)
Outdoor settings
    Home 51 (15%)
    Workplace 24 (6.8%)
    Education 8 (2.3%)
    Commercial 19 (5.4%)
    Healthcare 0 (0%)
    Leisure 83 (24%)
    Other 166 (47%)
Outdoor-Indoor mixed settings
    Covered patio or terrace 20 (8.1%)
    Semi-open corridor/gallery 11 (4.5%)
    Balcony 3 (1.2%)
    Veranda 5 (2.0%)
    Atrium 1 (0.4%)
    Transportation (car/taxi) 138 (56%)
    Transportation (bus or commuter/regional rail) 31 (13%)
    Transportation (long-distance train) 6 (2.4%)
    Transportation (underground, subway) 6 (2.4%)
    Transportation (airplane) 6 (2.4%)
    Transportation (bike) 5 (2.0%)
    Transportation (ferry) 1 (0.4%)
    Other 14 (5.7%)
1 n (%) 
gtsave(event_tbl_setting |> as_gt(), "tables/table2.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec4997ccbbf6e.html screenshot completed
event_tbl_indoors <- 
events_complete |> 
  select(setting_specific
         ) |> 
  tbl_summary(missing = "no"
  )

event_tbl_indoors
Characteristic N = 1,7631
Specific indoor setting
    Bathroom 86 (9.6%)
    Bedroom 114 (13%)
    Break/lounge area 6 (0.7%)
    Classroom 4 (0.4%)
    Conference/meeting room 18 (2.0%)
    Convenience store/supermarkt 23 (2.6%)
    Corridor 10 (1.1%)
    Dentist 1 (0.1%)
    Drug store 1 (0.1%)
    Home office 85 (9.5%)
    Kitchen 100 (11%)
    Laboratory 2 (0.2%)
    Lecture hall 2 (0.2%)
    Library 2 (0.2%)
    Living room 213 (24%)
    Office supply store 1 (0.1%)
    Open-plan office area 16 (1.8%)
    Other 80 (8.9%)
    Parking garage 10 (1.1%)
    Personal workspace/desk 72 (8.0%)
    Restaurant/cafeteria/bakery 32 (3.6%)
    Shopping mall 16 (1.8%)
    Studio 1 (0.1%)
1 n (%) 
gtsave(event_tbl_indoors |> as_gt(), "tables/table3.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec49924850ec4.html screenshot completed
event_tbl_wear <- 
events_complete |> 
  select(type, wear_activity, nonwear_activity, nighttime
         ) |> 
  tbl_summary(
    missing = "no"
  )

event_tbl_wear
Characteristic N = 1,7631
Wear type: Are you wearing the light logger at the moment?
    Wear 1,535 (87%)
    Non-wear 132 (7.5%)
    Bedtime 88 (5.0%)
Activity
    Sedentary 728 (47%)
    Light activity 693 (45%)
    Moderate activity 103 (6.7%)
    High-intensity activity 11 (0.7%)
Non-wear wearable position
    Dark stationary 13 (9.8%)
    Dark mobile 22 (17%)
    Stationary 88 (67%)
    Other 9 (6.8%)
Nightstand wearable measurement direction
    Upward 78 (89%)
    Downward 5 (5.7%)
    Other 5 (5.7%)
1 n (%) 
gtsave(event_tbl_wear |> as_gt(), "tables/table4.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec4991b8c7897.html screenshot completed
event_tbl_other <- 
events_complete |> 
  select(social_context, setting_light, scenario_daylight, 
         scenario_electric, autonomy
         ) |> 
  tbl_summary(
    missing_text = "Missing"
  )

event_tbl_other
Characteristic N = 1,7631
Are you alone or with others?
    Alone 1,028 (59%)
    With others 728 (41%)
    Missing 7
Select the setting
    Indoors 941 (61%)
    Outdoors 351 (23%)
    Mixed 247 (16%)
    Missing 224
Daylight conditions
    Away from window 363 (25%)
    Direct sunlight 216 (15%)
    Near a window 664 (46%)
    Shade / cloudy 210 (14%)
    Missing 310
Electric lighting conditions
    Dim light 242 (19%)
    Lights off 356 (28%)
    Lights on 658 (52%)
    Missing 507
autonomy
    Yes 825 (58%)
    Partly 172 (12%)
    No 428 (30%)
    Missing 338
1 n (%) 
gtsave(event_tbl_other |> as_gt(), "tables/table4.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec49923410fa2.html screenshot completed
event_tbl_duration <-
events_complete |> 
  # drop_na(setting_light) |>
  dplyr::mutate(setting_light = 
                  case_when(type == "Bedtime" ~ "Bed",
                            type == "Non-wear" ~ "Non-wear",
                            .default = setting_light |> as.character()),
                setting_light = 
                  setting_light |> factor(levels = c("Bed", "Indoors", "Mixed", "Outdoors", "Non-wear"))) |> 
  dplyr::summarize(`Daily duration` = sum(status.duration, na.rm = TRUE),
                   .by = c(setting_light, record_id)) |> 
  dplyr::summarize(`Daily duration` = mean(`Daily duration`),
                   .by = c(setting_light)) |> 
  dplyr::mutate(`Daily duration` =
                  `Daily duration` /
                  sum(as.numeric(`Daily duration`)) *
                  24*60*60,
                Percent = 
                  (as.numeric(`Daily duration`)/
                  sum(as.numeric(`Daily duration`)))
                  # vec_fmt_percent()
                ) |> 
  arrange(setting_light) |> 
  gt(rowname_col = "setting_light") |> 
  grand_summary_rows( 
                     fns = list(
      sum ~ sum(.)
    ),
    fmt = list(~ fmt_percent(., columns = Percent, decimals = 0), 
               ~ fmt_duration(., columns = `Daily duration`, 
               input_units = "secs",
               max_output_units = 2))
    ) |> 
  fmt_duration(`Daily duration`, 
               input_units = "secs",
               max_output_units = 2) |> 
  fmt_percent(columns = Percent, decimals = 0) |> 
  sub_missing() |> 
  tab_header(title = "Mean daily duration in condition")

event_tbl_duration
Mean daily duration in condition
Daily duration Percent
Bed 5h 59m 25%
Indoors 10h 6m 42%
Mixed 1h 56m 8%
Outdoors 1h 31m 6%
Non-wear 2h 59m 12%
— 1h 25m 6%
sum 1d 100% 
gtsave(event_tbl_duration, "tables/table5.png")
file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//Rtmp0jGklP/filec49920187aab.html screenshot completed

Combining Events with light data

In this step, we expand the light measurements with the event data. To this end, we need to specify start and end times for each log entry and thus state.

events_light <-
events_complete |> 
  rename(Id = device_id) |>
  group_by(Id) |> 
  dplyr::mutate(setting_light2 = setting_light,
                setting_light = 
                  case_when(type == "Bedtime" ~ "Bed",
                            type == "Non-wear" ~ "Non-wear",
                            .default = setting_light |> as.character()),
                setting_light = 
                  setting_light |> factor(levels = c("Bed", "Indoors", "Mixed", "Outdoors", "Non-wear")),
                end = case_when(
                  is.na(lead(Datetime)) ~ (end_dt + ddays(1)),
                  # Datetime >= end_dt ~ Datetime,
                  !is.na(lead(Datetime)) ~ lead(Datetime)
                ) |> as.POSIXct(tz = "UTC"),
                Id = factor(Id),
                .after = Datetime
                ) |> 
  rename(start = Datetime) |> 
  select(-c(age:file_path, status.duration, UTC_dt))

#adding event-specific information
light_data_ext <- 
light_data |> 
  select(Id, Datetime, Datetime_UTC, MEDI) |> 
  add_states(events_light, Datetime.colname = Datetime_UTC) |> 
  rename(DatetimeUTC = Datetime) |>
  rename(Datetime = Datetime_UTC) |> 
  unite(
    scenario_daylight2, setting_light, scenario_daylight, sep = ": ", remove = FALSE, na.rm = TRUE
  )  |>
  dplyr::mutate(
    scenario_daylight2 =
      case_when(setting_light %in% c("Outdoors", "Mixed", "Indoors", "Bed") &
                  !is.na(scenario_daylight) ~
                  paste(setting_light, scenario_daylight, sep = ": ")),
    scenario_electric2 = 
      case_when(setting_light %in% c("Outdoors", "Indoors", "Mixed", "Bed") &
                  !is.na(scenario_electric) ~
                  paste(setting_light, scenario_electric, sep = ": "))
  )

#adding participant-specific information
participant_data <- 
  data_devices_cleaned |> 
  select(-record_id, -data) |> 
  rename(Id = device_id) |> 
  dplyr::mutate(Id = factor(Id))

light_data_ext <-
  light_data_ext |> 
  left_join(participant_data, by = "Id")
participants <- light_data_ext |> pull(Id) |> levels()

light_data_ext |>
  gg_days(facetting = FALSE,
          y.axis.label = "Melanopic EDI (lx)") |>
  gg_state(setting_light, aes_fill = setting_light, alpha = 0.5) +
  scale_fill_manual(
    values = c(
      Bed = "darkgrey",
      `Non-wear` = "tomato",
      Indoors = "gold",
      Outdoors = "skyblue2",
      Mixed = "lightgreen"
    )
  ) + 
  facet_wrap(~Id, ncol = 5) +
  labs(fill = "Condition")

ggsave("figures/Fig3_overview.png", width = 10, height = 10, scale = 2)

light_data_ext |>
  filter(Id %in% participants[1:24]) |> 
  gg_days(facetting = FALSE,
          y.axis.label = "Melanopic EDI (lx)") |>
  gg_state(setting_light, aes_fill = setting_light, alpha = 0.5) +
  scale_fill_manual(
    values = c(
      Bed = "darkgrey",
      `Non-wear` = "tomato",
      Indoors = "gold",
      Outdoors = "skyblue2",
      Mixed = "lightgreen"
    )
  ) + 
  facet_wrap(~Id, ncol = 8) +
  labs(fill = "Condition")

ggsave("figures/Fig3a_overview.png", width = 14, height = 5, scale = 2)

light_data_ext |>
  filter(Id %in% participants[25:47]) |> 
  gg_days(facetting = FALSE,
          y.axis.label = "Melanopic EDI (lx)") |>
  gg_state(setting_light, aes_fill = setting_light, alpha = 0.5) +
  scale_fill_manual(
    values = c(
      Bed = "darkgrey",
      `Non-wear` = "tomato",
      Indoors = "gold",
      Outdoors = "skyblue2",
      Mixed = "lightgreen"
    )
  ) + 
  facet_wrap(~Id, ncol = 8) +
  labs(fill = "Condition")

ggsave("figures/Fig3b_overview.png", width = 14, height = 5, scale = 2)

State analysis

In this section, we look at melanopic EDI for various conditions.

First, we create a function that takes a variable and a filter variable for that column, and creates a histogram off that basis.

histogram_plot <- function(variable, filter) {
light_data_ext |> 
  # drop_na({{ variable }}) |> 
  filter({{ variable }} == filter) |>
  ggplot(aes(y=1, x = MEDI, slab_fill = after_stat(level))) +
  # geom_violin() +
  stat_histinterval(na.rm = TRUE, breaks = 20, align = align_center(at = 0))+
  stat_pointinterval(na.rm = TRUE, point_size = 10, colour = "red") +
  scale_x_continuous(trans = "symlog", breaks = c(0, 10^(0:5)),
                     labels = expression(0,10^0,10^1, 10^2, 10^3, 10^4, 10^5),
                     limits = c(0, 10^5)) +
  theme_void() +
  scale_color_manual(values = scales::brewer_pal()(3)[-1], aesthetics = "slab_fill") +
  theme_sub_axis_x(text = element_text(size = 30)) +
  guides(slab_fill = "none") +
  ylim(c(1,2))
}

The second plot function takes the same inputs, and creates a doubleplot showing when these instances occur.

light_data_1day <- 
light_data_ext |> 
  aggregate_Datetime(unit = "1 hour")

timeline_plot <- function(variable, filter) {
light_data_1day |> 
  dplyr::mutate(
    state = {{ variable }} == filter,
    state = ifelse(is.na(state), FALSE, state)
  ) |> 
  group_by(state) |>
  select(state, Datetime, MEDI) |> 
  aggregate_Date(
    date.handler = \(x) "2025-09-22",
    numeric.handler = \(x) median(x, na.rm = TRUE),
    n = n(),
    lower = quantile(MEDI, p = 0.25, na.rm = TRUE),
    upper = quantile(MEDI, p = 0.75, na.rm = TRUE)
  ) |> 
    dplyr::mutate(n = n/sum(n)) |> 
  gap_handler(full.days = TRUE) |> 
  gg_day(
    facetting = FALSE,
    geom = "blank",
    jco_color = FALSE, x.axis.breaks = hms::hms(hours = seq(0, 24, by = 6))
  ) +
  geom_ribbon(aes(ymin = lower, ymax = upper, fill = state), 
              alpha = 0.25
              ) +
  geom_hline(yintercept = 0) +
  geom_line(
    aes(colour = state), lwd = 0.5
  )+
  geom_point(
            aes(size = n, colour = state)) +
  scale_fill_manual(values = c(`TRUE` = "#EFC000FF",
                               `FALSE` = "darkgrey")) +
  scale_color_manual(values = c(`TRUE` = "#EFC000FF",
                                `FALSE` = "darkgrey")) +
  coord_cartesian(ylim = c(0, 10^5)) +
  scale_size_continuous(range = c(3, 20), limits = c(0,1)) +
  scale_alpha_continuous(range = c(0.01, 0.5)) +
  guides(lwd = "none", colour = "none", fill = "none", alpha = "none",
         size = "none") +
  theme_void() +
  theme_sub_axis(text = element_text(size = 30)) +
  theme_sub_plot(margin = margin(r = 35)) +
  scale_y_continuous(trans = "symlog", breaks = c(0, 10^(0:5)),
                     labels = expression(0,10^0,10^1, 10^2, 10^3, 10^4, 10^5),
                     limits = c(0, 10^5))
}

Next, we create a function that also takes a variable, and creates a table containing durations, episodes, and some key metrics.

duration_tibble <- function(variable) {
  instances <-
    light_data_ext |>
    # group_by() |>
    extract_states({{ variable }}) |>
    group_by({{ variable }}) |>
    summarize_numeric(prefix = "",
                      remove = c("start", "end", "epoch"))
  # drop_na()
  
  metrics <-
    light_data_ext |>
    group_by({{ variable }}) |>
    dplyr::summarize(
      mean = mean(MEDI, na.rm = TRUE),
      min = min(MEDI, na.rm = TRUE),
      q025 = quantile(MEDI, probs = 0.025, na.rm = TRUE),
      q250 = quantile(MEDI, probs = 0.17, na.rm = TRUE),
      median = median(MEDI, na.rm = TRUE),
      q750 = quantile(MEDI, probs = 0.83, na.rm = TRUE),
      q975 = quantile(MEDI, probs = 0.975, na.rm = TRUE),
      max = max(MEDI, na.rm = TRUE),
    )
  # drop_na()
  
  variable_chr <- rlang::ensym(variable) |> as_string()
  
  left_join(instances, metrics, by = variable_chr) |> 
  drop_na()
}

Lastly, a function to create the actual table. It takes both the duration_tibble() and histogram_plot() functions and creates a gt html table based on it.

duration_table <- function(variable) {
  
  variable_chr <- rlang::ensym(variable) |> as_string()
  
  duration_tibble({{ variable }}) |>
    arrange(desc(median)) |> 
    gt(rowname_col = variable_chr) |>
    fmt_number(mean:max, decimals = 0) |>
    cols_add(Plot = {{ variable }}, 
             Plot2 = {{ variable }},
             .after = max) |>
    cols_add(rel_duration = total_duration/sum(total_duration), 
             .after = total_duration) |>
    fmt_percent(rel_duration, decimals = 0) |> 
    cols_label(
      duration = "Episode duration",
      total_duration = "Total duration",
      episodes = "Episodes",
      mean = "Mean",
      min = "0%",
      q025 = "2.5%",
      q250 = "17%",
      median = "Median",
      q750 = "83%",
      q975 = "97.5%",
      max = "100%",
      Plot = "Histogram",
      Plot2 = "Timeline",
      rel_duration = "Relative duration"
    ) |>
    text_transform(locations = cells_body(Plot), fn = \(x) {
      gt::ggplot_image({
        x %>%
          map(\(y) histogram_plot({{ variable }}, y))
      }, height = gt::px(75), aspect_ratio = 2)
    }) |>
    text_transform(locations = cells_body(Plot2), fn = \(x) {
      gt::ggplot_image({
        x %>%
          map(\(y) timeline_plot({{ variable }}, y))
      }, height = gt::px(75), aspect_ratio = 2)
    }) |>
    fmt_duration(2:3, input_units = "seconds", max_output_units = 2) |>
    tab_footnote(
      "Scaled histograms show 66% of data in dark blue, 95% of data in dark & light blue, and the most extreme 5% of data in grey. The median is shown as a red dot.",
      locations = cells_column_labels(Plot)
    ) |>
    tab_footnote(
      "Timeline plots show the median of the condition (yellow dot) against the rest of the data (grey dot). Colored bands show the interquartile range. Size of the dots indicates the relative number of instances, i.e., large dots indicate many occurances at a given time (relative to other times).",
      locations = cells_column_labels(Plot2)
    ) |>
    tab_footnote(
      "Average duration of an episode in this category. eps. = Episode(s)",
      locations = cells_column_labels(duration)
    ) |> 
    tab_footnote(
      "An episode refers to a log entry in a category, until a different entry",
      locations = cells_column_labels(episodes)
    ) |> 
    # cols_units(mean = "lx",
    #            median = "lx") |>
    gt::tab_spanner("Distribution", min:max) |> 
    tab_footnote(
      "Melanopic equivalent daylight illuminance (lx)",
      locations = list(cells_column_labels(c(mean)),
                       cells_column_spanners()
      )
    ) |> 
    cols_merge_n_pct(total_duration, rel_duration) |> 
    cols_merge(
      c(duration, episodes), pattern = "{1} ({2} eps.)"
    ) |> 
    cols_align("center") |> 
    tab_style(
      style = cell_fill(color = "grey"),
      locations = list(#cells_body(columns = c(min, max)),
                       cells_column_labels(c(min, max)))
    ) |> 
    tab_style(
      style = cell_fill(color = "tomato"),
      locations = list(#cells_body(median),
                       cells_column_labels(median))
    ) |> 
    tab_style(
      style = cell_text(weight = "bold"),
      locations = list(cells_body(median),
                       cells_stub(),
                       cells_column_labels(median),
                       cells_column_spanners())
    ) |> 
    tab_style(
      style = cell_fill(color = "#4880B8"),
      locations = list(#cells_body(columns = c(q250, q750)),
                       cells_column_labels(c(q250, q750)))
    ) |> 
    tab_style(
      style = cell_fill(color = "#C2DAE9"),
      locations = list(#cells_body(columns = c(q025, q975)),
                       cells_column_labels(c(q025, q975)))
    ) |> 
      cols_move_to_end(c(mean, duration, total_duration)) |> 
    tab_header("Summary of melanopic EDI across log entries",
               subtitle = glue("Question/Characteristic: {label(light_data_ext[[variable_chr]])}")
    ) |> 
    tab_style(
              style = cell_text(color = "red3"), 
              locations = cells_stub(
              # columns = total_duration, # the column whose text you want to style
              rows    = rel_duration < 0.05   # condition using another column
    )) |> 
        tab_footnote(
      "Red indicates this category represents < 5% of entries",
      locations = list(cells_stub(rows    = rel_duration < 0.05)
      )
    )
}
label(light_data_ext$setting_light) <- "What is your general context?"
label(light_data_ext$autonomy) <- "Were the lighting conditions in this setting self-selected?"
label(light_data_ext$setting_indoors_workingspace) <- "Indoors setting (work)"
label(light_data_ext$scenario_daylight2) <- "Daylight conditions (stratified)"
label(light_data_ext$scenario_electric2) <- "Electric lighting conditions (stratified)"
label(light_data_ext$country) <- "Country"
label(light_data_ext$sex) <- "Sex"

c("social_context", "type", "setting_light", "setting_light2", "country", "sex", "autonomy", 
  "setting_indoors", "setting_location", "setting_outdoors", "setting_mixed",
  "nighttime", "nonwear_activity", "type.detail", "setting_specific", "setting_indoors_home", "setting_indoors_workingspace", "setting_indoors_healthfacility", "setting_indoors_learningfacility", "setting_indoors_retailfacility", "scenario_daylight", "scenario_electric", "screen_phone", "screen_tablet", "screen_pc", "screen_tv", "behaviour_change", "travel_time_zone", "scenario_daylight2", "scenario_electric2", "wear_activity"
  )|>
  walk(\(x) {
    symbol <- sym(x)
    table <- duration_table(!!symbol)
    table
    gtsave(table, glue("tables/table_duration_{x}.png"), vwidth = 1200)
    })
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Time above threshold

In this section we calculate the time above threshold for the single day of 22 September 2025 across latitude and country.

TAT250 <- 
light_data_ext |> 
  filter_Date(start = "2025-09-22", length = "1day") |> 
  fill(latitude, longitude, city, country, .direction = "downup") |> 
  dplyr::summarize(
    duration_above_threshold(
      MEDI, Datetime, threshold = 250, na.rm = TRUE,
      as.df = TRUE
    ),
    latitude = unique(latitude),
    longitude = unique(longitude),
    city = unique(city),
    country = unique(country)
  ) |> 
  mutate(n = n(), .by = country)

TAT250_plot <- function(value){
TAT250 |> 
  ggplot(aes(x= fct_reorder(Id, duration_above_250), y = duration_above_250))+
  geom_col(aes(fill = {{ value }})) +
  scale_fill_viridis_b(labels = \(x) paste0(x, "°"))+
  scale_y_time(labels = \(x) x |> as_datetime(tz = "UTC") |> format("%H:%M"),
               expand = FALSE) + 
  theme_minimal() +
  theme_sub_axis_x(text = element_blank(), line = element_line()) +
  theme_sub_panel(grid.major.x = element_blank())+
  labs(x = "Participants", y = "Time above 250lx mel EDI (HH:MM)")
}

# TAT250_plot(longitude) / (TAT250_plot(latitude) + ylim(c(10*3600, 0))) + plot_layout(axis = "collect", heights = c(1,0.05,1))

TAT250_plot(latitude)

ggsave("figures/Fig4_TAT250.png", width = 5, height = 2, scale = 1.5)
TAT250 |> 
  ggplot(aes(y= fct_reorder(country, duration_above_250), x = duration_above_250))+
  geom_boxplot(aes(col = n)) +
  scale_color_viridis_b(breaks = c(1, 3, 5, 10, 15))+
  scale_x_time(labels = \(x) x |> as_datetime(tz = "UTC") |> format("%H:%M")) + 
  theme_minimal() +
  # theme_sub_axis_x(text = element_blank(), line = element_line()) +
  theme_sub_panel(grid.major.y = element_blank())+
  labs(y = NULL, x = "Time above 250lx mel EDI (HH:MM)")

ggsave("figures/Fig5_TAT250_country.png", width = 4, height = 3, scale = 1.5)

Global perspectives

In this section we look at the global distribution of melanopic EDI, Time above 250lx, and the Dose of light.

Shade data

In this section we calculate day and night times around the globe. As we want to use this information for a looped visualization, we will set a slightly different cutoff and only collect 48 hours.

# Time window (UTC)
t_start <- ymd_hms("2025-09-21 12:00:00", tz = "UTC")
t_end   <- ymd_hms("2025-09-23 12:00:00", tz = "UTC")
# Step between frames (adjust to taste: "30 mins", "1 hour", etc.)
time_step <- "15 mins"

# Spatial grid resolution (degrees). 2° keeps things light; 1° looks smoother but is heavier.
lon_step <- 0.5
lat_step <- 0.5

# Darkness mapping: fully dark at civil twilight (-6°), linearly increasing from 0 to 1 as altitude goes 0 -> -6°.
dark_full_altitude <- -6 # degrees

# ---------- build lon/lat grid ----------
lons <- seq(-180, 180, by = lon_step)
lats <- seq(-90,  90,  by = lat_step)
grid <- expand.grid(lon = lons, lat = lats) |>
  as_tibble()

# ---------- time sequence ----------
times_utc <- seq(t_start, t_end, by = time_step)

# ---------- compute sun altitude for each (lon, lat, time) ----------
# We'll loop over time slices (efficient enough for this grid size),
# compute altitude (in radians from suncalc), convert to degrees,
# then map to a "darkness" alpha value.
compute_slice <- function(tt) {
  
  # build a data frame with the timestamp repeated for each grid point
  df <- data.frame(
    date = rep(tt, nrow(grid)),
    lat  = grid$lat,
    lon  = grid$lon
  )
  
  # call suncalc with a data frame instead of separate lat/lon vectors
  sp <- suncalc::getSunlightPosition(data = df, keep = c("altitude"))
  
  # altitude is returned in radians; convert to degrees
  alt_deg <- sp$altitude * 180 / pi
  
  # darkness mapping (same as before)
  darkness <- pmin(1, pmax(0, -alt_deg / 6))
  
  tibble(
    lon = grid$lon,
    lat = grid$lat,
    time = tt,
    alt_deg = alt_deg,
    darkness = darkness
  )
}
# Create the progress bar once
pb <- progress_bar$new(
  format = "Computing slices [:bar] :percent eta: :eta",
  total = length(times_utc), clear = FALSE, width = 70
)

shade_list <- vector("list", length(times_utc))

for (i in seq_along(times_utc)) {
  # Compute one time slice (without pb$tick() inside)
  shade_list[[i]] <- compute_slice(times_utc[[i]])
  # Now update the bar
  pb$tick()
}

shade_df <- dplyr::bind_rows(shade_list)

shade_df <- bind_rows(lapply(times_utc, compute_slice))

Melanopic EDI

Light data

light_data_globe <- 
light_data_ext |> 
    filter_Datetime(DatetimeUTC, start = t_start, end = t_end, tz = "UTC") |> 
    select(Id, DatetimeUTC, latitude, longitude, MEDI) |> 
    dplyr::mutate(lat2 = plyr::round_any(latitude, 12), 
           lon2 = plyr::round_any(longitude, 12)) |> 
    ungroup() |> 
    dplyr::summarize(
        .by = c(DatetimeUTC, lat2, lon2),
        lat = mean(latitude),
        lon = mean(longitude),
        MEDI = mean(MEDI, na.rm = TRUE),
        n = n()
    ) |> 
    rename(time = DatetimeUTC)

light_globe <- st_as_sf(light_data_globe, coords = c("lon", "lat"), crs = 4326)
light_proj <- st_transform(light_globe, crs = "+proj=eqc")

Plot

p <- 
  ggplot() +
  geom_sf(
    data = world_proj,
    fill = "grey",
    color = NA,
    size = 0.25,
    alpha = 0.5,
    show.legend = FALSE
  ) +
  geom_sf(data = tz_lines,
          colour = "deepskyblue3",
          alpha = 0.75,
          linewidth = 0.1) +
  geom_tile(
    data = shade_df,
    mapping = aes(x = lon, y = lat, alpha = darkness),
    fill = "black",
    width = lon_step, height = lat_step
  ) +
  geom_sf(
    data = light_proj,
    aes(size = n, fill = MEDI, colour = MEDI),
    alpha = 0.9,
    shape = 21,
    stroke = 0.2
  ) +
  # tile overlay for night shading (black with varying alpha)
  geom_sf_text(
    data = locations_proj,
    aes(label = n),
    size = 1.9,
    fontface = 2,
    color = "white",
    alpha = 0.75
  ) +
  scale_alpha(range = c(0, 0.6), limits = c(0, 1), guide = "none") +
  coord_sf(crs = 4326, expand = FALSE, xlim = c(-180, 180), ylim = c(-90, 90)) +
  # coord_sf(crs = 4326, xlim = c(-180, 180), ylim = c(-90, 90)) +
  labs(
    title = "{format(frame_time, '%Y-%m-%d %H:%M:%S')} (UTC)",
    x = NULL, 
    y = NULL
  ) +
  guides(size = "none",
        )+
  labs(fill = "mel EDI (lx)", colour = "mel EDI (lx)")+
  scale_fill_viridis_b(trans = "symlog", breaks = c(0, 10^(0:5)),
                       labels = 
                         function(x) format(x, scientific = FALSE, big.mark = " "),
                       limits = c(0, 10^5)) +
  scale_colour_viridis_b(trans = "symlog", breaks = c(0, 10^(0:5)),
                       labels = 
                         function(x) format(x, scientific = FALSE, big.mark = " "),
                       limits = c(0, 10^5)) +
  scale_size_continuous(range = c(3, 7.5)) +
  theme_minimal(base_size = 13) +
  theme(
    panel.grid.major = element_line(size = 0.1, colour = "grey85"),
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    legend.text.align = 1,
    legend.text.position = "left",
    legend.position = "inside",
    legend.position.inside = c(0.1,0.35),
    legend.background = element_rect(fill = alpha("white", alpha = 0.25),
                                     colour = NA)
  ) +
  transition_time(time)

# Frame rate and dimensions
fps_out <- 12
width_px <- 1400
height_px <- 800

if(interactive()) {

anim <- animate(
  p,
  duration = 48,
  fps = fps_out,
  width = width_px,
  height = height_px,
  res = 150,
  renderer = gifski_renderer(loop = TRUE)
)

out_file <- "figures/Fig6_melEDI_global.gif"

anim_save(out_file, animation = anim)

}

Export for dashboard

In this section we export key data for the dashboard.

save(
     data_devices,
     events_complete,
     light_data_ext,
     histogram_plot,
     light_data_1day,
     timeline_plot,
     duration_tibble,
     duration_table,
     TAT250,
     file = "data/cleaned/dashboard.RData")